where \(y_i = \max _{q_j \in \cal {Q}} sim(emb(\hat{q_i}), emb(q_j))\) , \(\hat{y}_i = g(\theta ,\hat{q_i})\)  is the score predicted by the re-ranker, \(D\)  refers to the training data, \(n = |D|\) , and \(sim\)  calculates the cosine similarity between the sentence embeddings \(emb(\hat{q_i})\)  and \(emb(q_j)\)  of \(\hat{q_i}\)  and \(q_j\) , respectively. We use Sentence-BERT [1]} to compute \(sim\) .
